Abstract

To protect native biodiversity and habitats from the negative impacts of biological invasions, comprehensive studies and measures to anticipate invasions are required, especially across countries in a transfrontier context. Species distribution models (SDMs) can be particularly useful to integrate different types of data and predict the distribution of invasive species across borders, both for current conditions and under scenarios of future environmental changes. We used SDMs to test whether predicting invasions and potential spatial conflicts with protected areas in a transfrontier context, under current and future climatic conditions, would provide additional insights on the patterns and drivers of invasion when compared to models obtained from predictions for individual regions/countries (different modelling strategies). The framework was tested with the invasive alien plant Acacia dealbata in North of Portugal/NW Spain Euro-region, where the species is predicted to increase its distribution under future climatic conditions. While SDMs fitted in a transfrontier context and using “the national strategy (with Portugal calibration data) presented similar patterns, the distribution of the invasive species was higher in the former. The transfrontier strategy expectedly allowed to capture a more complete and accurate representation of the species’ niche. Predictions obtained in a transfrontier context are therefore more suitable to support resource prioritisation for anticipation and monitoring impacts of biological invasions, while also providing additional support for international cooperation when tackling issues of global change. Our proposed framework provided useful information on the potential patterns of invasion by A. dealbata in a transfrontier context, with an emphasis on protected areas. This information is crucial for decision-makers focusing on the prevention of invasions by alien species inside protected areas in a transfrontier context, opening a new way for collaborative management of invasions.

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